Here it is represented an image analysis technique using both noising & de-noising process. By taking a simple image of different formats we added a noise i. e. Gaussian noise to the particular image and then the calculation of SNR(SIGNAL-TO-NOISE RATIO) and PSNR(PEAK SIGNAL-TO-NOISE RATIO) is performed based on the image formats such as jpeg, png, etc. Although there are various types of noise...

De-noising is one of the most important applications of image processing which has been applied to a wide variety of real world problems. De-noising allows for improving image quality in imaging modalities that are noise prone. A lot of research work has gone in to improving quality of 2D images using various de-noising techniques but new modalities of imaging such as industrial 3D X-ray comput...

Partial discharge (PD) activity measurements have been carried out by selecting noise signals (de-noising) using Support Vector Machine (SVM)and then recognized Convolutional Neural Network (CNN). CNN testing was various models such as activation methods: Sigmoid, Softmax, Relu, Tanh, Swish. Number of layers used is 1, 2, 3, 4 with filter sizes 32, 64, 128, 256 and kernel 3x3, 2x2, 1x1, 1x2, 1x...

seismic waves are non-stationary due to its propagation through the earth. time-frequency transformsare suitable tools for analyzing non-stationary seismic signals. spectral decomposition can reveal thenon-stationary characteristics which cannot be easily observed in the time or frequency representationalone. various types of spectral decomposition methods have been introduced by some researche...

Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in images. Image de-noising has become an integral part image processing workflow. It is used attenuate noises and accentuate specific within. Machine learning important tool image-de-noising workflow terms its robustness, accuracy, time requirement. This paper explores numerous state-of-the-art ma...

Active infrared thermography is an attractive and highly reliable technique used for the non-destructive evaluation of test objects. In this paper, defect detection on subsurface STS304 metal specimen was performed by applying line-scanning method to induction thermography. general, camera are fixed in thermography, but can excite a uniform heat source because relative movement occurs. After th...

Abstract—Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise le...

In this work, a model of a corporate network has been developed, simulated and implemented using optimized network engineering tool technology in a simulation environment of 100m x 100m office network topology. We monitored delay traffic, used one dimensional multilevel wavelet de-noising technique to filter the possible cause of the network congestion and used ACF and FFT to validate the resul...

Feature noising is an effective mechanism on reducing the risk of overfitting. To avoid an explosive searching space, existing work typically assumes that all features share a single noise level, which is often cross-validated. In this paper, we present a Bayesian feature noising model that flexibly allows for dimension-specific or group-specific noise levels, and we derive a learning algorithm...

The de-noising is a challenging task in the field of signal and image processing. Any natural image corrupted by gussian noise can be de-noised using wavelet method. Wavelet-based image denoising is an important technique in the area of image noise reduction. Wavelets have their natural ability to represent images in a very sparse form which is the foundation of wavelet-based denoising through ...